@@ -24,9 +24,9 @@ copyright = '2018, xpqiu' | |||
author = 'xpqiu' | |||
# The short X.Y version | |||
version = '0.5.0' | |||
version = '0.4.5' | |||
# The full version, including alpha/beta/rc tags | |||
release = '0.5.0' | |||
release = '0.4.5' | |||
# -- General configuration --------------------------------------------------- | |||
@@ -97,10 +97,10 @@ Part IV: DataSetLoader举例 | |||
在MatchingLoader类当中我们封装了一个对数据集中的文本内容进行进一步的预处理的函数: | |||
:meth:`~fastNLP.io.data_loader.matching.MatchingLoader.process` | |||
这个函数具有各种预处理option,如: | |||
- 是否将文本转成全小写 | |||
- 是否需要序列长度信息,需要什么类型的序列长度信息 | |||
- 是否需要用BertTokenizer来获取序列的WordPiece信息 | |||
- 等等 | |||
- 是否将文本转成全小写 | |||
- 是否需要序列长度信息,需要什么类型的序列长度信息 | |||
- 是否需要用BertTokenizer来获取序列的WordPiece信息 | |||
- 等等 | |||
具体内容参见 :meth:`fastNLP.io.MatchingLoader.process` 。 | |||
@@ -178,71 +178,71 @@ sampler | |||
* SequentialSampler: 顺序取出元素的采样器【无初始化参数】 | |||
* RandomSampler:随机化取元素的采样器【无初始化参数】 | |||
以下代码使用BucketSampler作为:class:`~fastNLP.DataSetIter`初始化的输入,运用:class:`~fastNLP.DataSetIter`自己写训练程序 | |||
以下代码使用BucketSampler作为 :class:`~fastNLP.DataSetIter` 初始化的输入,运用 :class:`~fastNLP.DataSetIter` 自己写训练程序 | |||
.. code-block:: python | |||
from fastNLP import BucketSampler | |||
from fastNLP import DataSetIter | |||
from fastNLP.models import CNNText | |||
from fastNLP import Tester | |||
import torch | |||
import time | |||
embed_dim = 100 | |||
model = CNNText((len(vocab),embed_dim), num_classes=3, padding=2, dropout=0.1) | |||
def train(epoch, data, devdata): | |||
optimizer = torch.optim.Adam(model.parameters(), lr=0.001) | |||
lossfunc = torch.nn.CrossEntropyLoss() | |||
batch_size = 32 | |||
.. code-block:: python | |||
# 定义一个Batch,传入DataSet,规定batch_size和去batch的规则。 | |||
# 顺序(Sequential),随机(Random),相似长度组成一个batch(Bucket) | |||
train_sampler = BucketSampler(batch_size=batch_size, seq_len_field_name='seq_len') | |||
train_batch = DataSetIter(batch_size=batch_size, dataset=data, sampler=train_sampler) | |||
start_time = time.time() | |||
print("-"*5+"start training"+"-"*5) | |||
for i in range(epoch): | |||
loss_list = [] | |||
for batch_x, batch_y in train_batch: | |||
optimizer.zero_grad() | |||
output = model(batch_x['words']) | |||
loss = lossfunc(output['pred'], batch_y['target']) | |||
loss.backward() | |||
optimizer.step() | |||
loss_list.append(loss.item()) | |||
#这里verbose如果为0,在调用Tester对象的test()函数时不输出任何信息,返回评估信息; 如果为1,打印出验证结果,返回评估信息 | |||
#在调用过Tester对象的test()函数后,调用其_format_eval_results(res)函数,结构化输出验证结果 | |||
tester_tmp = Tester(devdata, model, metrics=AccuracyMetric(), verbose=0) | |||
res=tester_tmp.test() | |||
print('Epoch {:d} Avg Loss: {:.2f}'.format(i, sum(loss_list) / len(loss_list)),end=" ") | |||
print(tester._format_eval_results(res),end=" ") | |||
print('{:d}ms'.format(round((time.time()-start_time)*1000))) | |||
loss_list.clear() | |||
train(10, train_data, dev_data) | |||
#使用tester进行快速测试 | |||
tester = Tester(test_data, model, metrics=AccuracyMetric()) | |||
tester.test() | |||
这段代码的输出如下:: | |||
-----start training----- | |||
Epoch 0 Avg Loss: 1.09 AccuracyMetric: acc=0.480787 58989ms | |||
Epoch 1 Avg Loss: 1.00 AccuracyMetric: acc=0.500469 118348ms | |||
Epoch 2 Avg Loss: 0.93 AccuracyMetric: acc=0.536082 176220ms | |||
Epoch 3 Avg Loss: 0.87 AccuracyMetric: acc=0.556701 236032ms | |||
Epoch 4 Avg Loss: 0.78 AccuracyMetric: acc=0.562324 294351ms | |||
Epoch 5 Avg Loss: 0.69 AccuracyMetric: acc=0.58388 353673ms | |||
Epoch 6 Avg Loss: 0.60 AccuracyMetric: acc=0.574508 412106ms | |||
Epoch 7 Avg Loss: 0.51 AccuracyMetric: acc=0.589503 471097ms | |||
Epoch 8 Avg Loss: 0.44 AccuracyMetric: acc=0.581068 529174ms | |||
Epoch 9 Avg Loss: 0.39 AccuracyMetric: acc=0.572634 586216ms | |||
[tester] | |||
AccuracyMetric: acc=0.527426 | |||
from fastNLP import BucketSampler | |||
from fastNLP import DataSetIter | |||
from fastNLP.models import CNNText | |||
from fastNLP import Tester | |||
import torch | |||
import time | |||
embed_dim = 100 | |||
model = CNNText((len(vocab),embed_dim), num_classes=3, padding=2, dropout=0.1) | |||
def train(epoch, data, devdata): | |||
optimizer = torch.optim.Adam(model.parameters(), lr=0.001) | |||
lossfunc = torch.nn.CrossEntropyLoss() | |||
batch_size = 32 | |||
# 定义一个Batch,传入DataSet,规定batch_size和去batch的规则。 | |||
# 顺序(Sequential),随机(Random),相似长度组成一个batch(Bucket) | |||
train_sampler = BucketSampler(batch_size=batch_size, seq_len_field_name='seq_len') | |||
train_batch = DataSetIter(batch_size=batch_size, dataset=data, sampler=train_sampler) | |||
start_time = time.time() | |||
print("-"*5+"start training"+"-"*5) | |||
for i in range(epoch): | |||
loss_list = [] | |||
for batch_x, batch_y in train_batch: | |||
optimizer.zero_grad() | |||
output = model(batch_x['words']) | |||
loss = lossfunc(output['pred'], batch_y['target']) | |||
loss.backward() | |||
optimizer.step() | |||
loss_list.append(loss.item()) | |||
#这里verbose如果为0,在调用Tester对象的test()函数时不输出任何信息,返回评估信息; 如果为1,打印出验证结果,返回评估信息 | |||
#在调用过Tester对象的test()函数后,调用其_format_eval_results(res)函数,结构化输出验证结果 | |||
tester_tmp = Tester(devdata, model, metrics=AccuracyMetric(), verbose=0) | |||
res=tester_tmp.test() | |||
print('Epoch {:d} Avg Loss: {:.2f}'.format(i, sum(loss_list) / len(loss_list)),end=" ") | |||
print(tester._format_eval_results(res),end=" ") | |||
print('{:d}ms'.format(round((time.time()-start_time)*1000))) | |||
loss_list.clear() | |||
train(10, train_data, dev_data) | |||
#使用tester进行快速测试 | |||
tester = Tester(test_data, model, metrics=AccuracyMetric()) | |||
tester.test() | |||
这段代码的输出如下:: | |||
-----start training----- | |||
Epoch 0 Avg Loss: 1.09 AccuracyMetric: acc=0.480787 58989ms | |||
Epoch 1 Avg Loss: 1.00 AccuracyMetric: acc=0.500469 118348ms | |||
Epoch 2 Avg Loss: 0.93 AccuracyMetric: acc=0.536082 176220ms | |||
Epoch 3 Avg Loss: 0.87 AccuracyMetric: acc=0.556701 236032ms | |||
Epoch 4 Avg Loss: 0.78 AccuracyMetric: acc=0.562324 294351ms | |||
Epoch 5 Avg Loss: 0.69 AccuracyMetric: acc=0.58388 353673ms | |||
Epoch 6 Avg Loss: 0.60 AccuracyMetric: acc=0.574508 412106ms | |||
Epoch 7 Avg Loss: 0.51 AccuracyMetric: acc=0.589503 471097ms | |||
Epoch 8 Avg Loss: 0.44 AccuracyMetric: acc=0.581068 529174ms | |||
Epoch 9 Avg Loss: 0.39 AccuracyMetric: acc=0.572634 586216ms | |||
[tester] | |||
AccuracyMetric: acc=0.527426 | |||
@@ -1,3 +1,121 @@ | |||
===================== | |||
Metric 教程 | |||
===================== | |||
===================== | |||
在进行训练时,fastNLP提供了各种各样的 :mod:`~fastNLP.core.metrics` 。 | |||
如 :doc:`/user/quickstart` 中所介绍的,:class:`~fastNLP.AccuracyMetric` 类的对象被直接传到 :class:`~fastNLP.Trainer` 中用于训练 | |||
.. code-block:: python | |||
from fastNLP import Trainer, CrossEntropyLoss, AccuracyMetric | |||
trainer = Trainer(model=model, train_data=train_data, dev_data=dev_data, | |||
loss=CrossEntropyLoss(), metrics=AccuracyMetric()) | |||
trainer.train() | |||
除了 :class:`~fastNLP.AccuracyMetric` 之外,:class:`~fastNLP.SpanFPreRecMetric` 也是一种非常见的评价指标, | |||
例如在序列标注问题中,常以span的方式计算 F-measure, precision, recall。 | |||
另外,fastNLP 还实现了用于抽取式QA(如SQuAD)的metric :class:`~fastNLP.ExtractiveQAMetric`。 | |||
用户可以参考下面这个表格,点击第一列查看各个 :mod:`~fastNLP.core.metrics` 的详细文档。 | |||
.. csv-table:: | |||
:header: 名称, 介绍 | |||
:class:`~fastNLP.core.metrics.MetricBase` , 自定义metrics需继承的基类 | |||
:class:`~fastNLP.core.metrics.AccuracyMetric` , 简单的正确率metric | |||
:class:`~fastNLP.core.metrics.SpanFPreRecMetric` , "同时计算 F-measure, precision, recall 值的 metric" | |||
:class:`~fastNLP.core.metrics.ExtractiveQAMetric` , 用于抽取式QA任务 的metric | |||
更多的 :mod:`~fastNLP.core.metrics` 正在被添加到 fastNLP 当中,敬请期待。 | |||
------------------------------ | |||
定义自己的metrics | |||
------------------------------ | |||
在定义自己的metrics类时需继承 fastNLP 的 :class:`~fastNLP.core.metrics.MetricBase`, | |||
并覆盖写入 ``evaluate`` 和 ``get_metric`` 方法。 | |||
evaluate(xxx) 中传入一个批次的数据,将针对一个批次的预测结果做评价指标的累计 | |||
get_metric(xxx) 当所有数据处理完毕时调用该方法,它将根据 evaluate函数累计的评价指标统计量来计算最终的评价结果 | |||
以分类问题中,Accuracy计算为例,假设model的forward返回dict中包含 `pred` 这个key, 并且该key需要用于Accuracy:: | |||
class Model(nn.Module): | |||
def __init__(xxx): | |||
# do something | |||
def forward(self, xxx): | |||
# do something | |||
return {'pred': pred, 'other_keys':xxx} # pred's shape: batch_size x num_classes | |||
假设dataset中 `label` 这个field是需要预测的值,并且该field被设置为了target | |||
对应的AccMetric可以按如下的定义, version1, 只使用这一次:: | |||
class AccMetric(MetricBase): | |||
def __init__(self): | |||
super().__init__() | |||
# 根据你的情况自定义指标 | |||
self.corr_num = 0 | |||
self.total = 0 | |||
def evaluate(self, label, pred): # 这里的名称需要和dataset中target field与model返回的key是一样的,不然找不到对应的value | |||
# dev或test时,每个batch结束会调用一次该方法,需要实现如何根据每个batch累加metric | |||
self.total += label.size(0) | |||
self.corr_num += label.eq(pred).sum().item() | |||
def get_metric(self, reset=True): # 在这里定义如何计算metric | |||
acc = self.corr_num/self.total | |||
if reset: # 是否清零以便重新计算 | |||
self.corr_num = 0 | |||
self.total = 0 | |||
return {'acc': acc} # 需要返回一个dict,key为该metric的名称,该名称会显示到Trainer的progress bar中 | |||
version2,如果需要复用Metric,比如下一次使用AccMetric时,dataset中目标field不叫label而叫y,或者model的输出不是pred:: | |||
class AccMetric(MetricBase): | |||
def __init__(self, label=None, pred=None): | |||
# 假设在另一场景使用时,目标field叫y,model给出的key为pred_y。则只需要在初始化AccMetric时, | |||
# acc_metric = AccMetric(label='y', pred='pred_y')即可。 | |||
# 当初始化为acc_metric = AccMetric(),即label=None, pred=None, fastNLP会直接使用'label', 'pred'作为key去索取对 | |||
# 应的的值 | |||
super().__init__() | |||
self._init_param_map(label=label, pred=pred) # 该方法会注册label和pred. 仅需要注册evaluate()方法会用到的参数名即可 | |||
# 如果没有注册该则效果与version1就是一样的 | |||
# 根据你的情况自定义指标 | |||
self.corr_num = 0 | |||
self.total = 0 | |||
def evaluate(self, label, pred): # 这里的参数名称需要和self._init_param_map()注册时一致。 | |||
# dev或test时,每个batch结束会调用一次该方法,需要实现如何根据每个batch累加metric | |||
self.total += label.size(0) | |||
self.corr_num += label.eq(pred).sum().item() | |||
def get_metric(self, reset=True): # 在这里定义如何计算metric | |||
acc = self.corr_num/self.total | |||
if reset: # 是否清零以便重新计算 | |||
self.corr_num = 0 | |||
self.total = 0 | |||
return {'acc': acc} # 需要返回一个dict,key为该metric的名称,该名称会显示到Trainer的progress bar中 | |||
``MetricBase`` 将会在输入的字典 ``pred_dict`` 和 ``target_dict`` 中进行检查. | |||
``pred_dict`` 是模型当中 ``forward()`` 函数或者 ``predict()`` 函数的返回值. | |||
``target_dict`` 是DataSet当中的ground truth, 判定ground truth的条件是field的 ``is_target`` 被设置为True. | |||
``MetricBase`` 会进行以下的类型检测: | |||
1. self.evaluate当中是否有varargs, 这是不支持的. | |||
2. self.evaluate当中所需要的参数是否既不在 ``pred_dict`` 也不在 ``target_dict`` . | |||
3. self.evaluate当中所需要的参数是否既在 ``pred_dict`` 也在 ``target_dict`` . | |||
除此以外,在参数被传入self.evaluate以前,这个函数会检测 ``pred_dict`` 和 ``target_dict`` 当中没有被用到的参数 | |||
如果kwargs是self.evaluate的参数,则不会检测 | |||
self.evaluate将计算一个批次(batch)的评价指标,并累计。 没有返回值 | |||
self.get_metric将统计当前的评价指标并返回评价结果, 返回值需要是一个dict, key是指标名称,value是指标的值 | |||
@@ -56,7 +56,7 @@ __all__ = [ | |||
"cache_results" | |||
] | |||
__version__ = '0.4.0' | |||
__version__ = '0.4.5' | |||
from .core import * | |||
from . import models | |||